CN109878341B - Intelligent network control method and system for new energy vehicle - Google Patents
Intelligent network control method and system for new energy vehicle Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
Abstract
The invention discloses a new energy vehicle intelligent network control method and a system thereof, comprising the following steps: collecting a detection signal and an electric quantity signal; the neural network control system acquires the detection signal and the electric quantity signal, and carries out deep data learning on the detection signal and the electric quantity signal, wherein the deep data learning is a parameter model which is constructed in real time and updates the running state, and the division is carried out according to the parameter model to obtain a division result; carrying out data operation analysis on the obtained detection signal and the electric quantity signal, and obtaining a real-time parameter; and performing state evaluation and classification processing on the real-time parameters obtained by data operation analysis according to the division results of the parameter model. The invention can stably and reliably utilize wind energy, solar energy, mechanical energy and the storage battery pack as power sources of the pure electric vehicle, effectively relieve energy crisis and protect atmospheric environment, can prolong endurance mileage and shorten the occupied time of charging, and has good self-adaptive learning characteristic and robustness.
Description
Technical Field
The invention relates to the technical field of renewable energy application, in particular to an intelligent network control method and system for a new energy vehicle.
Background
In recent years, the shortage of energy and the environmental pollution are increasingly serious, the renewable energy sources are superior to wind energy and solar energy, the wind energy and the solar energy are both clean, pollution-free and renewable, and the renewable energy sources are always hot points of research in the automobile industry, and the renewable energy sources can reduce the dependence on non-renewable energy sources such as petroleum and coal, relieve the energy crisis and protect the atmospheric environment.
At present, although there are a lot of vehicle-mounted wind power and solar energy combined power generation systems, these systems still have some defects in the aspects of energy conversion efficiency, high cost, process, technology, intellectualization and the like, and the existing vehicle-mounted wind power generation device still can additionally increase the driving resistance of an automobile, and the purpose of wind power generation is achieved by increasing the energy consumption of a pure electric vehicle, so that the requirements of energy saving and endurance cannot be met.
Because the automobile intelligent neural network control technology and the pure electric vehicle technology are not completely mature, some problems still exist: the travel intelligence does not meet the requirements of people, the vehicle-mounted networking still cannot adapt to the automobile intelligence, and the stable and good intelligent self-adaptive learning and control are lacked, the vehicle-mounted chip technology cannot meet the requirements of the intelligence, even the automatic updating of real-time vehicle-mounted navigation is not mature, the self-adaptive learning characteristic of the system is poor, the self-checking system of the vehicle equipment has limitations, and the robustness is poor; the cost of the two storage batteries and the motor controller is high, the storage batteries are long in charging time, short in service life and frequent in replacement, the maintenance cost is high, and the driving range is short; and thirdly, a perfect service station and enough charging equipment are lacked.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an intelligent network control method and system for a new energy vehicle.
The technical scheme is as follows:
the intelligent network control method for the new energy vehicle comprises the following steps:
detecting the running states of the wind power generation device, the photovoltaic solar power generation device, the rotating power generation device, the temperature and humidity monitoring device and the voltage stabilizing device and converting the running states into detection signals;
detecting the electric quantity state stored in the storage battery pack and converting the electric quantity state into an electric quantity signal;
collecting a detection signal and an electric quantity signal;
the neural network control system acquires the detection signal and the electric quantity signal, and carries out deep data learning on the detection signal and the electric quantity signal, wherein the deep data learning is a parameter model which is constructed in real time and updates the running state, and the division is carried out according to the parameter model to obtain a division result;
performing data operation analysis on the acquired detection signal and the electric quantity signal, and obtaining real-time parameters of the detection signal and the electric quantity signal;
and performing state evaluation and classification processing on the real-time parameters obtained by data operation analysis according to the division results of the parameter model.
According to the intelligent network control method for the new energy vehicle, the data deep learning is to construct and update a parameter model of an operation state in real time according to parameters of a corresponding historical data state of an Ethernet remote server.
According to the intelligent network control method for the new energy vehicle, the deep data learning not only constructs and updates a parameter model of an operation state in real time according to parameters of a corresponding historical data state of the Ethernet remote server, but also can update the constructed parameter model in real time according to the parameters of the historical data state processed by the neural network control system.
According to the intelligent network control method of the new energy vehicle, the division result according to the parameter model is as follows:
dividing the running states of the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device into a normal power generation state and an abnormal power generation state according to a parameter model;
dividing the running state of the voltage stabilizer into a normal running state and an abnormal running state according to a parameter model;
and dividing the running state of the temperature and humidity monitoring device into a real-time numerical value normal state and a real-time numerical value abnormal state according to the parameter model.
According to the intelligent network control method for the new energy vehicle, the state evaluation and classification processing is performed according to the dividing result of the parameter model, and the temperature value and the humidity value of the area where the vehicle is located and the real-time line condition to be passed are combined:
when one or more of the running states of the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device is evaluated and classified as an abnormal power generation state, or the running state of the voltage stabilizing device is evaluated and classified as an abnormal running state, or the running state of the temperature and humidity monitoring device is evaluated and classified as a real-time numerical value abnormal state, data abnormal state information is output.
According to the intelligent network control method for the new energy vehicle, the display module and the voice module acquire output data abnormal state information, the display module displays the data abnormal state information through the display screen, and the voice module prompts the abnormal state information in a voice broadcasting mode.
The intelligent network control system of the new energy vehicle comprises a neural network control system, a wind power generation device, a photovoltaic solar power generation device, a rotating power generation device, a temperature and humidity monitoring device, a storage battery pack, a voltage stabilizing device, a voice device and a display screen; the neural network control system comprises a networking module, the wind power generation device comprises a wind power detection module, the photovoltaic solar power generation device comprises a solar detection module, the rotating power generation device comprises a rotating detection module, the temperature and humidity monitoring device comprises a blank filter module, the storage battery pack comprises an electric quantity detection module, the voltage stabilizing device comprises a voltage stabilizing module, the voice device comprises a voice module, and the display screen comprises a display module; the wind energy detection module, the solar energy detection module, the rotation detection module, the air filtering module, the electric quantity detection module, the voltage stabilization module, the voice module and the display module are all electrically connected with the neural network control system;
the networking module is used for accessing the Ethernet; the system comprises a neural network control system, a historical data state acquisition module, a data processing module and a data processing module, wherein the neural network control system is used for acquiring parameters of the historical data state corresponding to the Ethernet remote server and acquiring temperature values and humidity values of the area where a vehicle provided with the neural network control system is located and real-time line conditions to be passed through;
the wind energy detection module is used for detecting the running state of the wind energy generating device and converting the running state into a detection signal;
the solar detection module is used for detecting the running state of the photovoltaic solar power generation device and converting the running state into a detection signal;
the rotation detection module is used for detecting the running state of the rotation power generation device and converting the running state into a detection signal;
the air filtering module is used for detecting the running state of the temperature and humidity monitoring device and converting the running state into a detection signal;
the electric quantity detection module is used for detecting the electric quantity state stored in the storage battery pack and converting the electric quantity state into an electric quantity signal;
the voice module is used for acquiring data abnormal state information; the system is used for prompting the abnormal state information;
the display module is used for acquiring data abnormal state information and displaying the data abnormal state information;
the voltage stabilizing device is used for stabilizing the voltage of the current and transmitting the current to the storage battery pack; and the voltage stabilizing module is used for detecting the running state of the voltage stabilizing device by detecting the stabilized voltage state and converting the running state into a detection signal.
The intelligent network control system for the new energy vehicle comprises a wind power generation device, and further comprises an air inlet pipeline, a first impeller assembly, a second impeller assembly, an air collecting pipeline and an exhaust pipeline, wherein the air inlet pipeline is used for inputting air flow and guiding the air flow to the first impeller assembly, the first impeller assembly is used for rotating and compressing the air flow, the air collecting pipeline is used for conveying the compressed air flow, the second impeller assembly is used for rotating, and the first impeller assembly and the second impeller assembly are also used for driving a coil to cut a magnetic field while rotating.
The photovoltaic solar power generation device comprises a motor, a lifting support and two crystalline silicon solar panels, wherein the solar detection module is used for detecting the running state of the crystalline silicon solar panels and converting the running state into a detection signal, the motor is used for operating and controlling the running of the lifting support, the lifting support is used for supporting the crystalline silicon solar panels, and the two crystalline silicon solar panels are stacked mutually.
The novel energy vehicle intelligent network control system is characterized in that the rotating power generation device further comprises an air blower, a rotor, a front wheel half shaft, a rear wheel half shaft and a miniature permanent magnet generator, wherein the air blower is used for driving the rotor to rotate, and the front wheel half shaft and the rear wheel half shaft are used for driving the miniature permanent magnet generator to generate power while rotating.
The intelligent network control system for the new energy vehicle comprises an RBF neural network and DSP microprocessing:
the RBF neural network is used for carrying out data deep learning on the detection signals and the electric quantity signals, constructing and updating a parameter model of the running state in real time, dividing according to the parameter model and obtaining a dividing result; the parameter model is used for updating the constructed parameter model in real time according to the parameters of the historical data state processed by the neural network control system; the system is used for carrying out data operation analysis on the detection signal and the electric quantity signal and obtaining real-time parameters of the detection signal and the electric quantity signal; the real-time parameters are used for carrying out state evaluation and classification processing according to the division results;
the DSP microprocessing is used for dividing the running state into a normal power generation state, an abnormal power generation state, a normal running state, an abnormal running state, a real-time numerical value normal state and a real-time numerical value abnormal state according to the parameter model; for outputting data exception status information.
The new energy vehicle intelligent network control system comprises a first impeller assembly and a second impeller assembly, wherein the first impeller assembly and the second impeller assembly respectively comprise a first impeller rotating shaft and a plurality of first blades, each first blade is arranged on the first impeller rotating shaft, each first blade is of an arc-shaped structure, and the first impeller rotating shaft is used for driving the first blades to rotate.
New energy vehicle intelligence network control system, first impeller subassembly, second impeller subassembly all include second impeller pivot and many circles of second blade, and each second blade all centers on the setting on the outer wall of second impeller pivot, and the position between two adjacent circles of second blade is staggered, and each second blade all is the arc structure.
The following illustrates the advantages or principles of the invention:
the intelligent network control method of the new energy vehicle generates current through the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device, stores the current into the storage battery pack and uses the current as the power source of the pure electric vehicle, the storage battery can be charged under the irradiation of sufficient sunlight or common light and in the running process of the vehicle, the endurance mileage is greatly prolonged, the occupied time of charging is shortened, the renewable clean energy can also effectively relieve the energy crisis and protect the atmospheric environment, the neural network control system can carry out data deep learning on the acquired detection signals and electric quantity signals, the technology for establishing and updating the parameter model in real time through data deep learning can continuously adjust and correct data of the specific parameter model, has good self-adaptive learning characteristic and robustness, and enables the subsequent state evaluation and classification processing of the division result to be more intelligent.
Drawings
Fig. 1 is a flow chart of a new energy vehicle intelligent network control method according to a first embodiment of the invention;
FIG. 2 is a block diagram of a flow chart of a partitioning result according to a first embodiment of the present invention;
FIG. 3 is a block diagram illustrating a process of status evaluation and classification and prompt of abnormal status information according to a first embodiment of the present invention;
FIG. 4 is a block diagram of a data deep learning process according to a first embodiment of the present invention;
FIG. 5 is a front view of a first impeller shaft and first blades in accordance with a first embodiment of the present invention;
FIG. 6 is a front view of a second impeller shaft and second blades according to a second embodiment of the present invention;
description of reference numerals:
10. the impeller comprises a first impeller rotating shaft 20, a first blade 30, a second impeller rotating shaft 40 and a second blade.
Detailed Description
The following provides a detailed description of embodiments of the invention.
Example one
As shown in fig. 1 to 4, the intelligent network control method for the new energy vehicle includes the following steps: detecting the running states of the wind power generation device, the photovoltaic solar power generation device, the rotating power generation device, the temperature and humidity monitoring device and the voltage stabilizing device and converting the running states into detection signals; detecting the electric quantity state stored in the storage battery pack and converting the electric quantity state into an electric quantity signal; collecting a detection signal and an electric quantity signal; the neural network control system acquires the detection signal and the electric quantity signal, and carries out deep data learning on the detection signal and the electric quantity signal, wherein the deep data learning is a parameter model which is constructed in real time and updates the running state, and the division is carried out according to the parameter model to obtain a division result; performing data operation analysis on the acquired detection signal and the electric quantity signal, and obtaining real-time parameters of the detection signal and the electric quantity signal; and performing state evaluation and classification processing on the real-time parameters obtained by data operation analysis according to the division results of the parameter model.
The deep learning of the data is to construct and update a parameter model of an operation state in real time according to parameters of a corresponding historical data state of the Ethernet remote server; in addition to establishing and updating the parameter model of the running state in real time according to the parameters of the corresponding historical data state of the Ethernet remote server, the deep data learning of the neural network control system can also update the established parameter model in real time according to the parameters of the historical data state processed by the neural network control system.
The new energy vehicle intelligent network control method comprises the following steps according to the division result of a parameter model: dividing the running states of the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device into a normal power generation state and an abnormal power generation state according to a parameter model; dividing the running state of the voltage stabilizer into a normal running state and an abnormal running state according to a parameter model; and dividing the running state of the temperature and humidity monitoring device into a real-time numerical value normal state and a real-time numerical value abnormal state according to the parameter model.
The state evaluation and classification processing is carried out according to the dividing result of the parameter model and also in combination with the temperature value and the humidity value of the area where the vehicle is located and the real-time line condition to be passed: when one or more of the running states of the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device is evaluated and classified as an abnormal power generation state, or the running state of the voltage stabilizing device is evaluated and classified as an abnormal running state, or the running state of the temperature and humidity monitoring device is evaluated and classified as a real-time numerical value abnormal state, data abnormal state information is output.
The display module and the voice module acquire the output data abnormal state information, the display module displays the data abnormal state information through the display screen, and the voice module prompts the abnormal state information in a voice broadcasting mode.
In this embodiment, the specific data operation analysis and state evaluation classification processing includes:
the data operation analysis is to determine the specific numerical value of the input real-time parameter by a least square method, and evaluate and classify the state;
subtracting the estimated detection signal and electric quantity signal from the real detection signal and electric quantity signal through a fitting curve of a least square method, then squaring the real detection signal and electric quantity signal integrally, and solving a specific numerical value of a real-time parameter according to an equation set;
and performing state evaluation and classification according to the specific numerical values of the real-time parameters and the division results of the parameter models, and controlling the running states of all devices in the intelligent network control system of the new energy vehicle.
The intelligent network control system of the new energy vehicle comprises a neural network control system, a wind power generation device, a photovoltaic solar power generation device, a rotating power generation device, a temperature and humidity monitoring device, a storage battery pack, a voltage stabilizing device, a voice device and a display screen; the neural network control system comprises a networking module, the wind power generation device comprises a wind power detection module, the photovoltaic solar power generation device comprises a solar detection module, the rotating power generation device comprises a rotating detection module, the temperature and humidity monitoring device comprises a blank filter module, the storage battery pack comprises an electric quantity detection module, the voltage stabilizing device comprises a voltage stabilizing module, the voice device comprises a voice module, and the display screen comprises a display module; the wind energy detection module, the solar energy detection module, the rotation detection module, the air filtering module, the electric quantity detection module, the voltage stabilization module, the voice module and the display module are all electrically connected with the neural network control system;
the neural network control system is used for acquiring the detection signal and the electric quantity signal, performing data deep learning on the detection signal and the electric quantity signal, constructing and updating a parameter model of the running state in real time, dividing according to the parameter model and obtaining a division result; the neural network control system comprises an RBF neural network and DSP microprocessing: the RBF neural network is used for carrying out data deep learning on the detection signals and the electric quantity signals, constructing and updating a parameter model of the running state in real time, dividing according to the parameter model and obtaining a dividing result; the parameter model is used for updating the constructed parameter model in real time according to the parameters of the historical data state processed by the neural network control system; the system is used for carrying out data operation analysis on the detection signal and the electric quantity signal and obtaining real-time parameters of the detection signal and the electric quantity signal; the real-time parameters are used for carrying out state evaluation and classification processing according to the division results;
the DSP microprocessing is used for dividing the running state into a normal power generation state, an abnormal power generation state, a normal running state, an abnormal running state, a real-time numerical value normal state and a real-time numerical value abnormal state according to the parameter model; for outputting data exception status information.
The RBF neural network of the present embodiment employs an adaptive law RBF neural network to estimate an unknown nonlinear function with arbitrarily small errors. The method mainly comprises two aspects, one is the data structure design of an intelligent neural network control system, namely the hidden layer nodes for reasonably building corresponding subsystems. The other is the data parameter design of the corresponding subsystem, namely the real-time parameter is solved. The RBF neural network has the advantages of strong input and output mapping functions, good approximation performance (application of a function approximation method), no local minimum problem, high convergence speed of time sequence prediction, classification and system control and learning processes and the like.
The networking module is used for accessing the Ethernet; the system comprises a neural network control system, a historical data state acquisition module, a data processing module and a data processing module, wherein the neural network control system is used for acquiring parameters of the historical data state corresponding to the Ethernet remote server and acquiring temperature values and humidity values of the area where a vehicle provided with the neural network control system is located and real-time line conditions to be passed through; the wind energy detection module is used for detecting the running state of the wind energy generating device and converting the running state into a detection signal; the solar detection module is used for detecting the running state of the photovoltaic solar power generation device and converting the running state into a detection signal; the rotation detection module is used for detecting the running state of the rotation power generation device and converting the running state into a detection signal; the air filtering module is used for detecting the running state of the temperature and humidity monitoring device and converting the running state into a detection signal; the electric quantity detection module is used for detecting the electric quantity state stored in the storage battery pack and converting the electric quantity state into an electric quantity signal; the voice module is used for acquiring data abnormal state information; the system is used for prompting the abnormal state information; the display module is used for acquiring data abnormal state information and displaying the data abnormal state information; the voltage stabilizing device is used for stabilizing the voltage of the current and transmitting the current to the storage battery pack; and the voltage stabilizing module is used for detecting the running state of the voltage stabilizing device by detecting the stabilized voltage state and converting the running state into a detection signal.
The new energy vehicle intelligent network control system comprises a motor, a lifting support and two crystalline silicon solar panels, wherein the solar detection module is used for detecting the running state of the crystalline silicon solar panels and converting the running state into a detection signal, the motor is used for operating and controlling the lifting support, the lifting support is used for supporting the crystalline silicon solar panels, and the two crystalline silicon solar panels are stacked mutually.
New energy vehicle intelligence network control system, wind power generation set are still including intake stack, first impeller subassembly, second impeller subassembly, collection wind pipeline and exhaust duct, the intake stack is used for the input of air current and guides the air current to first impeller subassembly, first impeller subassembly is used for rotating and compresses the air current, and collection wind pipeline is used for carrying the air current after the compression, and the second impeller subassembly is used for rotating, and first impeller subassembly and second impeller subassembly still are used for driving the coil cutting magnetic field when the pivoted.
Referring to fig. 5, each of the first impeller assembly and the second impeller assembly includes a first impeller rotating shaft 10 and a plurality of first blades 20, each of the first blades 20 is disposed on the first impeller rotating shaft 10, each of the first blades 20 is in an arc-shaped structure, and the first impeller rotating shaft 10 is used for driving the first blade 20 to rotate.
The rotating power generation device of the intelligent network control system of the new energy vehicle further comprises an air blower, a rotor, a front wheel half shaft, a rear wheel half shaft and a miniature permanent magnet generator, wherein the air blower is used for driving the rotor to rotate, and the front wheel half shaft and the rear wheel half shaft are used for driving the miniature permanent magnet generator to generate power while rotating.
The working principle of the wind power generation device is as follows: when the wind energy is less than 2m/s, the wind energy detection module of the neural network control system does not detect the voltage value set by the wind energy generating device, and the wind energy generating device automatically stops running to prevent power consumption; when the relative speed of wind energy relative to a vehicle is more than 2m/s, the wind power generation device starts to generate electricity, when the vehicle speed is more than 10km/h, the neural network control system controls the wind power detection module to detect the running states of the air inlet pipeline, the first impeller component, the second impeller component, the air collecting pipeline and the air exhaust pipeline and convert the running states into detection signals, and the working principle of the wind power generation device is as follows: the air current is input from the air inlet pipeline and is guided to the first impeller assembly, the air current drives the first impeller assembly to rotate and is compressed by the first impeller assembly, the compressed air current flows through the air collecting pipeline and flows into the second impeller assembly, the compressed air current drives the second impeller assembly to rotate, the first impeller assembly and the second impeller assembly drive the coil to cut the magnetic field to generate electricity while rotating, and the generated current flows into the voltage stabilizing device.
The working principle of the photovoltaic solar device is as follows: when the crystalline silicon solar panel is irradiated by sufficient sunlight or common light, the photovoltaic solar device starts to work, light energy is converted into electric energy through the action of a photovoltaic effect, and the generated current flows into the voltage stabilizing device; the lifting support can also perform secondary lifting adjustment on the crystalline silicon solar panel so as to increase the irradiation area of the crystalline silicon solar panel and improve the charging efficiency of the photovoltaic solar device, and the photovoltaic solar device automatically stops running to prevent power consumption under the condition that the sunlight is insufficient in cloudy days, haze days, rainy days and at night;
lifting adjustment for the first time: when a vehicle stops in an open place and is irradiated by sufficient sunlight or common light, a motor drives a lifting support, the lifting support moves one of the crystalline silicon solar panels stacked above to the front of the vehicle and supports the crystalline silicon solar panel, and the lifting support raises the other crystalline silicon solar panel stacked below to the position of the crystalline silicon solar panel stacked above before the crystalline silicon solar panel is not moved;
and (3) second lifting adjustment: the motor continues to drive the lifting support, the lifting support moves the crystalline silicon solar panels which are stretched to the front and rotate to the position of a glass and a cover which are attached to the front of the vehicle, when the vehicle is driven, the motor drives the lifting support, and the lifting support resets the two crystalline silicon solar panels to be stacked mutually.
The working principle of the rotary power generation device is as follows: the rotation power generation device starts to operate, the front wheel half shaft and the rear wheel half shaft rotate, the micro permanent magnet generator is driven to generate electricity while rotating, generated current flows into the voltage stabilizing device, when an air conditioner in the vehicle is started, the blower drives the rotor to rotate, and the blower drives the generated current to flow into the voltage stabilizing device.
Temperature and humidity monitoring devices's theory of operation: the temperature and humidity monitoring device collects real-time temperature and humidity, converts the real-time temperature and humidity into electric signals and transmits the electric signals to the air filtering module, the air filtering module acquires the electric signals and triggers corresponding numerical operation to obtain numerical values of the real-time temperature and humidity and judge whether the numerical values are in a range suitable for bacteria propagation, if not, the numerical values are evaluated and classified as a real-time numerical value normal state, and the air filtering module is automatically closed after 3 minutes; if yes, the evaluation is classified as a real-time numerical abnormal state.
The current generated by the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device flows through the voltage stabilizing device, the voltage stabilizing device stabilizes the voltage of the flowing current and transmits the current to the storage battery.
The embodiment has the following advantages:
1. the intelligent network control method of the new energy vehicle generates current through the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device, stores the current into the storage battery pack and uses the current as the power source of the pure electric vehicle, the storage battery can be charged under the irradiation of sufficient sunlight or common light and in the running process of the vehicle, the endurance mileage is greatly prolonged, the occupied time of charging is shortened, the renewable clean energy can also effectively relieve the energy crisis and protect the atmospheric environment, the neural network control system can carry out data deep learning on the acquired detection signals and electric quantity signals, the technology for establishing and updating the parameter model in real time through data deep learning can continuously adjust and correct data of the specific parameter model, has good self-adaptive learning characteristic and robustness, and enables the subsequent state evaluation and classification processing of the division result to be more intelligent.
2. According to the parameter of the huge historical data state of the Ethernet remote server, the parameter model of the running state is built and updated in real time on the basis of big data, the parameter model is more accurate, and the parameter model is continuously adjusted and corrected while being built and updated in real time, so that the automatic environment adaptation, real-time intelligent control, good control approximation performance and global optimum characteristics of the intelligent network control method for the new energy vehicle are further realized.
3. When wind power generation set, photovoltaic solar power generation set, rotation power generation set, voltage regulator device, humiture monitoring devices work and operate, still detect the condition of running state, and automatic fault detection function makes the vehicle more artificial intelligence, further satisfies the demand of market to the new forms of energy vehicle.
4. And evaluation and classification of various parameters of the division result are carried out by combining the temperature value, the humidity value and the real-time road condition of the area, so that the state evaluation and classification processing is more accurate, and the accuracy of the output data abnormal state information is further ensured.
5. The abnormal state information is prompted in a display screen display and voice broadcasting mode, and the safety of the new energy vehicle intelligent network control method is further improved.
6. The deep learning of the data of the neural network control system can also continuously adjust and correct the parameter model according to the parameters of the historical data state processed by the neural network control system and the running environment and running conditions of the neural network control system, so that the self-adaptive environment is realized, and the self-adaptive learning characteristic and robustness of the intelligent network control method of the new energy vehicle are further enhanced.
7. The air current is imported by wind power generation set's air inlet pipe, and the air collecting pipe of flowing through discharges from the exhaust pipe, on the one hand, is favorable to reducing the air current resistance effect that the vehicle received to reduce the energy consumption, on the other hand, reduce the chassis of vehicle, promote the operating stability of vehicle.
8. The working principle of the photovoltaic solar power generation device is adjusted through secondary lifting, so that the utilization rate of light energy such as sunlight and common lamplight is effectively improved, and the crystalline silicon solar panel of the photovoltaic solar power generation device rotates to a position where the crystalline silicon solar panel is attached to glass and a vehicle cover in front of a vehicle, so that the good lighting effect at multiple angles is realized, and the privacy in the vehicle can be further protected; under the condition of insufficient light, the photovoltaic solar device automatically stops running to prevent power consumption, and further the photovoltaic solar device is more energy-saving and environment-friendly.
9. The air filtering module detects the running state of the temperature and humidity monitoring device, so that the temperature and humidity monitoring device ensures fresh air and no peculiar smell in the vehicle, and is favorable for caring the health of people in the vehicle.
10. The first impeller rotating shaft 10 requires less material for processing and manufacturing, so that the cost is further reduced, and the first blades 20 with the arc-shaped structures are more easily driven by airflow and rotate, so that the utilization efficiency of wind energy is improved.
Example two
As shown in fig. 6, in this embodiment, compared with the first embodiment, each of the first impeller assembly and the second impeller assembly includes a second impeller shaft 30 and a plurality of turns of second blades 40, each of the second blades 40 is disposed around the outer wall of the second impeller shaft 30, the positions of two adjacent turns of second blades 40 are staggered, and each of the second blades 40 has an arc-shaped structure.
The embodiment has the following advantages:
the second blades 40 of two adjacent circles of the embodiment are also arranged in a staggered manner, so that the wind energy utilization rate can be further improved, and the charging efficiency of the wind energy power generation device is further improved.
The above are merely specific embodiments of the present invention, and the scope of the present invention is not limited thereby; any alterations and modifications without departing from the spirit of the invention are within the scope of the invention.
Claims (5)
1. The intelligent network control method of the new energy vehicle is characterized by comprising the following steps:
detecting the running states of the wind power generation device, the photovoltaic solar power generation device, the rotating power generation device, the temperature and humidity monitoring device and the voltage stabilizing device and converting the running states into detection signals;
detecting the electric quantity state stored in the storage battery pack and converting the electric quantity state into an electric quantity signal;
collecting a detection signal and an electric quantity signal;
the method comprises the steps that a neural network control system obtains a detection signal and an electric quantity signal, and carries out deep data learning on the detection signal and the electric quantity signal, wherein the deep data learning is a parameter of a corresponding historical data state according to an Ethernet remote server, or a parameter model of an operation state is built and updated in real time according to the parameter of the historical data state processed by the neural network control system;
the neural network control system adopts an RBF neural network with self-adaptation law to estimate an unknown nonlinear function with any small error, and the estimation of the unknown nonlinear function comprises two aspects, namely building hidden layer nodes of corresponding subsystems and solving a parameter model, wherein the solution of the parameter model is the center and the variance of a radial basis function and the weight from the hidden layer to an output layer;
the neural network control system divides according to the parameter model to obtain a division result;
the division result according to the parametric model is as follows: dividing the running states of the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device into a normal power generation state and an abnormal power generation state according to a parameter model; dividing the running state of the voltage stabilizer into a normal running state and an abnormal running state according to a parameter model; dividing the running state of the temperature and humidity monitoring device into a real-time numerical value normal state and a real-time numerical value abnormal state according to a parameter model;
performing data operation analysis on the acquired detection signal and the electric quantity signal, and obtaining real-time parameters of the detection signal and the electric quantity signal;
performing state evaluation and classification processing on real-time parameters obtained by data operation analysis according to the division results of the parameter model;
the data operation analysis and state evaluation classification processing comprises the following steps:
subtracting the estimated detection signal and electric quantity signal according to the actual detection signal and electric quantity signal through a fitting curve of a least square method, then squaring the whole, and solving a specific numerical value of a real-time parameter according to an equation set;
according to the specific numerical value of the real-time parameter, performing state evaluation and classification according to the division result of the parameter model, and performing state evaluation and classification on the real-time parameter obtained by data operation analysis according to the division result of the parameter model;
the state evaluation and classification processing is carried out according to the dividing result of the parameter model and also in combination with the temperature value and the humidity value of the area where the vehicle is located and the real-time line condition to be passed:
and when one or more of the running states of the wind power generation device, the photovoltaic solar power generation device and the rotating power generation device is/are evaluated and classified as an abnormal power generation state, or the running state of the voltage stabilizing device is evaluated and classified as an abnormal running state, or the running state of the temperature and humidity monitoring device is evaluated and classified as a real-time numerical value abnormal state, outputting data abnormal state information.
2. The intelligent network control method for the new energy vehicle as claimed in claim 1, wherein the display module and the voice module acquire the output abnormal data state information, the display module displays the abnormal data state information through a display screen, and the voice module prompts the abnormal data state information in a voice broadcast mode.
3. The intelligent network control system of the new energy vehicle is applied to the intelligent network control method of the new energy vehicle according to any one of claims 1 to 2, and is characterized by comprising a neural network control system, a wind power generation device, a photovoltaic solar power generation device, a rotation power generation device, a temperature and humidity monitoring device, a storage battery pack, a voltage stabilizing device, a voice device and a display screen; the neural network control system comprises a networking module, the wind power generation device comprises a wind power detection module, the photovoltaic solar power generation device comprises a solar detection module, the rotating power generation device comprises a rotating detection module, the temperature and humidity monitoring device comprises a blank filter module, the storage battery pack comprises an electric quantity detection module, the voltage stabilizing device comprises a voltage stabilizing module, the voice device comprises a voice module, and the display screen comprises a display module; the wind energy detection module, the solar energy detection module, the rotation detection module, the air filtering module, the electric quantity detection module, the voltage stabilization module, the voice module and the display module are all electrically connected with the neural network control system;
the networking module is used for accessing the Ethernet; the system comprises a neural network control system, a historical data state acquisition module, a data processing module and a data processing module, wherein the neural network control system is used for acquiring parameters of the historical data state corresponding to the Ethernet remote server and acquiring temperature values and humidity values of the area where a vehicle provided with the neural network control system is located and real-time line conditions to be passed through;
the wind energy detection module is used for detecting the running state of the wind energy generating device and converting the running state into a detection signal;
the solar detection module is used for detecting the running state of the photovoltaic solar power generation device and converting the running state into a detection signal;
the rotation detection module is used for detecting the running state of the rotation power generation device and converting the running state into a detection signal;
the air filtering module is used for detecting the running state of the temperature and humidity monitoring device and converting the running state into a detection signal;
the electric quantity detection module is used for detecting the electric quantity state stored in the storage battery pack and converting the electric quantity state into an electric quantity signal;
the voice module is used for acquiring data abnormal state information; the system is used for prompting the abnormal state information;
the display module is used for acquiring data abnormal state information and displaying the data abnormal state information;
the voltage stabilizing device is used for stabilizing the voltage of the current and transmitting the current to the storage battery pack; the voltage stabilizing module is used for detecting the running state of the voltage stabilizing device by detecting the stabilized voltage state and converting the running state into a detection signal;
the wind power generation device also comprises an air inlet pipeline, a first impeller assembly, a second impeller assembly, an air collecting pipeline and an air exhaust pipeline, wherein the air inlet pipeline is used for inputting air flow and guiding the air flow to the first impeller assembly;
the first impeller assembly and the second impeller assembly respectively comprise a first impeller rotating shaft and a plurality of first blades, each first blade is arranged on the first impeller rotating shaft, each first blade is of an arc-shaped structure, and the first impeller rotating shaft is used for driving the first blades to rotate;
the photovoltaic solar power generation device also comprises a motor, a lifting support and two crystalline silicon solar panels, wherein the solar detection module is used for detecting the running state of the crystalline silicon solar panels and converting the running state into a detection signal;
the neural network control system comprises an RBF neural network and DSP microprocessing:
the RBF neural network is used for carrying out data deep learning on the detection signals and the electric quantity signals, and establishing and updating a parameter model of the running state in real time, wherein the data deep learning is that the established parameter model is updated in real time according to the parameters of the historical data state processed by the neural network control system; the system is used for carrying out data operation analysis on the detection signal and the electric quantity signal and obtaining real-time parameters of the detection signal and the electric quantity signal; the real-time parameters are used for carrying out state evaluation and classification processing according to the division results;
the DSP microprocessing is used for dividing the running state into a normal power generation state, an abnormal power generation state, a normal running state, an abnormal running state, a real-time numerical value normal state and a real-time numerical value abnormal state according to the parameter model; for outputting data exception status information.
4. The intelligent network control system for the new energy vehicle as claimed in claim 3, wherein the rotation power generation device further comprises a blower, a rotor, a front wheel half shaft, a rear wheel half shaft, and a micro permanent magnet generator, wherein the blower is used for driving the rotor to rotate, and the front wheel half shaft and the rear wheel half shaft are used for driving the micro permanent magnet generator to generate power while rotating.
5. The intelligent network control system for the new energy vehicle as claimed in any one of claims 3 or 4, wherein each of the first impeller assembly and the second impeller assembly comprises a second impeller rotating shaft and a plurality of circles of second blades, each second blade is arranged around the outer wall of the second impeller rotating shaft, the positions of two adjacent circles of second blades are staggered, and each second blade is in an arc-shaped structure.
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